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强噪声环境下自适应CRPF故障诊断方法

王进花 曹洁 李伟 黄玲

王进花, 曹洁, 李伟, 等 . 强噪声环境下自适应CRPF故障诊断方法[J]. 北京航空航天大学学报, 2018, 44(5): 923-930. doi: 10.13700/j.bh.1001-5965.2017.0353
引用本文: 王进花, 曹洁, 李伟, 等 . 强噪声环境下自适应CRPF故障诊断方法[J]. 北京航空航天大学学报, 2018, 44(5): 923-930. doi: 10.13700/j.bh.1001-5965.2017.0353
WANG Jinhua, CAO Jie, LI Wei, et al. An adaptive CRPF fault diagnosis method under strong noise condition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 923-930. doi: 10.13700/j.bh.1001-5965.2017.0353(in Chinese)
Citation: WANG Jinhua, CAO Jie, LI Wei, et al. An adaptive CRPF fault diagnosis method under strong noise condition[J]. Journal of Beijing University of Aeronautics and Astronautics, 2018, 44(5): 923-930. doi: 10.13700/j.bh.1001-5965.2017.0353(in Chinese)

强噪声环境下自适应CRPF故障诊断方法

doi: 10.13700/j.bh.1001-5965.2017.0353
基金项目: 

国家自然科学基金 61763028

甘肃省自然科学基金 1506RJZA105

甘肃省自然科学基金 1606RJZA145

详细信息
    作者简介:

    王进花  女, 博士研究生, 副教授。主要研究方向:故障诊断、非线性滤波方法及应用

    曹洁  女, 教授, 博士生导师。主要研究方向:智能信息处理、非线性理论及应用

    通讯作者:

    曹洁, E-mail: caoj@lut.cn

  • 中图分类号: TP277

An adaptive CRPF fault diagnosis method under strong noise condition

Funds: 

National Natural Science Foundation of China 61763028

Natural Science Foundation of Gansu Province, China 1506RJZA105

Natural Science Foundation of Gansu Province, China 1606RJZA145

More Information
  • 摘要:

    针对非线性非高斯系统在实际工作环境中受强噪声干扰影响导致的故障诊断精度低的问题,提出了一种状态转移密度方差自适应更新的代价评估粒子滤波(CRPF)故障诊断方法。通过设计观测值与先验状态之间的相关性判别函数,根据噪声和误差的大小实时自适应调整状态转移密度方差,增强算法对强噪声干扰的适应能力;研究了残差自适应阈值的设计方法,通过引入滑动窗求区间均值代替基于参数置信区间自适应阈值的均值和方差,在保证故障诊断准确性的前提下减少计算时间。以160 MW燃油机组为例,通过对不同强噪声环境下的汽包水位传感器故障诊断实例分析,结果表明该方法在复杂噪声环境下故障诊断的准确性得到了明显提高,同时减少了计算时间。

     

  • 图 1  伽马噪声背景下2种算法对x1x2的跟踪误差对比

    Figure 1.  Tracking error comparison of x1 and x2 between two algorithms under condition of Gamma noise

    图 2  高斯混合噪声背景下2种算法对x1x2的跟踪误差对比

    Figure 2.  Tracking error comparison of x1 and x2 between two algorithms under condition of Gaussian mixture noise

    图 3  自适应阈值对比

    Figure 3.  Comparison of adaptive thresholds

    图 4  伽马噪声背景下故障检测及隔离

    Figure 4.  Fault detection and isolation under condition of Gammanoise

    图 5  高斯混合噪声背景下故障检测及隔离

    Figure 5.  Fault detection and isolation under condition of Gaussian mixture noise

    表  1  不同噪声背景下x1x2的平均绝对误差

    Table  1.   Mean absolute errors of x1 and x2 under different noise conditions

    状态 伽马噪声 高斯混合噪声
    改进前 改进后 改进前 改进后
    x1 0.011 1 0.008 2 0.195 2 0.009 5
    x2 3.575 0 1.707 1 4.365 9 0.886 0
    下载: 导出CSV

    表  2  故障漏报率和时间计算复杂度比较

    Table  2.   Comparison of missed diagnosis rates and time computation complexity

    参数 CRPF故障诊断方法 ACRPF故障诊断方法
    文献[13-14] 本文 文献[13-14] 本文
    伽马噪声下漏报率 0.004 7 0.004 5 0.003 8 0.003 7
    高斯混合噪声下漏报率 0.006 3 0.006 1 0.004 5 0.004 6
    时间计算复杂度 O(n3) O(n2) O(n3) O(n2)
    下载: 导出CSV
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出版历程
  • 收稿日期:  2017-05-24
  • 录用日期:  2017-06-30
  • 网络出版日期:  2018-05-20

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